@InProceedings{SilvaGalvSant:2014:ReNeAp,
               author = "Silva, Ricardo Dal'Agnol da and Galv{\~a}o, L{\^e}nio Soares and 
                         Santos, Jo{\~a}o Roberto dos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Redes neurais aplicadas ao estudo de florestas prim{\'a}ria e 
                         secund{\'a}ria com dados espectral/textural ali/eo-1 / Neural 
                         networks applied to the study of primary and secondary forests 
                         with spectral/textural ali/eo-1 data",
            booktitle = "Anais...",
                 year = "2014",
                pages = "629--639",
         organization = "Semin{\'a}rio de Atualiza{\c{c}}{\~a}o em Sensoriamento Remoto 
                         e Sistemas de Informa{\c{c}}{\~o}es Geogr{\'a}ficas Aplicados 
                         {\`a} Engenharia Florestal, 11. (SenGeF).",
            publisher = "IEP",
              address = "Curitiba",
             keywords = "Florestas tropicais, sucess{\~o}es secund{\'a}rias, redes 
                         neurais artificiais, ALI/EO-1, textura GLCM, Tropical forests, 
                         secondary successions, artificial neural networks, ALI/EO-1, GLCM 
                         texture.",
             abstract = "As sucess{\~o}es secund{\'a}rias s{\~a}o tipologias importantes 
                         para a manuten{\c{c}}{\~a}o da biodiversidade, regime 
                         hidrol{\'o}gico e sequestro de carbono. A utiliza{\c{c}}{\~a}o 
                         de m{\'e}tricas texturais GLCM pode colaborar na 
                         discrimina{\c{c}}{\~a}o dessas classes por extrair a 
                         variabilidade espacial do dossel florestal. Assim sendo, 
                         tamb{\'e}m se faz necess{\'a}ria uma t{\'e}cnica como redes 
                         neurais artificiais para sele{\c{c}}{\~a}o dos atributos mais 
                         relevantes e integra{\c{c}}{\~a}o desses dados. O objetivo do 
                         presente estudo foi de avaliar e comparar o uso de atributos 
                         espectrais ALI/EO-1 e m{\'e}tricas texturais GLCM utilizando a 
                         t{\'e}cnica de redes neurais artificiais Multi-Layer Perceptron 
                         para mapeamento da cobertura da terra na Floresta Nacional do 
                         Tapaj{\'o}s e arredores, com foco na discrimina{\c{c}}{\~a}o 
                         das tipologias florestais prim{\'a}rias e sucess{\~o}es 
                         secund{\'a}rias. Observou-se que os atributos texturais mais 
                         relevantes foram a textura m{\'e}dia das bandas 3, 4, 6, 7 e 8, e 
                         textura dissimilaridade da banda 8. Esses atributos, ao serem 
                         integrados aos dados espectrais em um conjunto h{\'{\i}}brido, 
                         proporcionaram uma melhor discrimina{\c{c}}{\~a}o entre as 
                         classes de NPV e solo, culturas agr{\'{\i}}colas e SS1/SS2, SS1 
                         e SS2, SS2 e SS3/FP. Dessa forma, as {\'a}reas de SS1, SS2, SS3 e 
                         FP puderam ser discriminadas com 89, 63, 62 e 83% de 
                         acur{\'a}cia. Constatou-se exatid{\~a}o global de 89% para a 
                         utiliza{\c{c}}{\~a}o dos dados h{\'{\i}}bridos contra 79% para 
                         dados somente espectrais. ABSTRACT Secondary successions are 
                         important typologies for biodiversity maintenance, hydrological 
                         regimen, and carbon sequestration. The use of GLCM textural 
                         metrics can collaborate to discriminate these classes due to the 
                         extraction of the spatial variability of the forest canopy. Hence, 
                         it is also necessary a technique such as artificial neural 
                         networks to select the most relevant attributes and to integrate 
                         these data. The aim of this study was to evaluate and compare the 
                         use of ALI/EO-1 spectral attributes and GLCM textural metrics 
                         using the Multi-Layer Perceptron artificial neural networks 
                         technique for land cover mapping in the Tapajos National Forest 
                         and vicinity, focusing on the discrimination of primary forest and 
                         secondary successions. It was observed that the most important 
                         textural attributes were the mean texture of bands 3, 4, 6, 7 and 
                         8, and the dissimilarity of band 8. These attributes, when 
                         integrated into the spectral data to compose a hybrid dataset, 
                         provided better discrimination between the classes of NPV and 
                         soil, crops and SS1/SS2, SS1 and SS2, SS2 and SS3/PF. Thereby, the 
                         SS1, SS2, SS3 and FP areas could be discriminated with 89, 63, 62 
                         and 83% of classification accuracy. It was observed an overall 
                         accuracy of 89% using the hybrid dataset against 79% using only 
                         the spectral data.",
  conference-location = "Curitiba",
      conference-year = "14-16 out. 2014",
                 issn = "2178-8634",
                label = "lattes: 6150479997891841 1 SilvaGalvSant:2014:ReNeAp",
             language = "pt",
           targetfile = "lenio redes.pdf",
        urlaccessdate = "20 abr. 2024"
}